17385659. SIGNALING FOR ADDITIONAL TRAINING OF NEURAL NETWORKS FOR MULTIPLE CHANNEL CONDITIONS simplified abstract (QUALCOMM Incorporated)

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SIGNALING FOR ADDITIONAL TRAINING OF NEURAL NETWORKS FOR MULTIPLE CHANNEL CONDITIONS

Organization Name

QUALCOMM Incorporated

Inventor(s)

Pavan Kumar Vitthaladevuni of San Diego CA (US)

Taesang Yoo of San Diego CA (US)

Naga Bhushan of San Diego CA (US)

SIGNALING FOR ADDITIONAL TRAINING OF NEURAL NETWORKS FOR MULTIPLE CHANNEL CONDITIONS - A simplified explanation of the abstract

This abstract first appeared for US patent application 17385659 titled 'SIGNALING FOR ADDITIONAL TRAINING OF NEURAL NETWORKS FOR MULTIPLE CHANNEL CONDITIONS

Simplified Explanation

The patent application describes a method for wireless communication using a neural network trained for different signal to noise ratios (SNRs) of a wireless communication channel. The method involves training the neural network based on the channel estimate and reporting the trained network parameters to the base station.

  • The method receives a configuration from a base station to train a neural network for multiple SNRs of a channel estimate.
  • It determines if the current SNR of the channel estimate is above a threshold value.
  • The neural network is trained based on the channel estimate to obtain a first trained neural network.
  • The channel estimate is perturbed to obtain a perturbed channel estimate.
  • The neural network is trained again based on the perturbed channel estimate to obtain a second trained neural network.
  • The parameters of both trained neural networks, along with the channel estimate, are reported to the base station.

Potential Applications

  • This method can be applied in wireless communication systems to improve the accuracy of channel estimation.
  • It can be used in various wireless communication technologies such as 5G, Wi-Fi, and IoT networks.

Problems Solved

  • The method addresses the challenge of accurately estimating the channel conditions in wireless communication systems.
  • It solves the problem of training a neural network for different SNRs of a channel estimate.

Benefits

  • By training the neural network for multiple SNRs, the method improves the robustness and adaptability of the network.
  • The method allows for more accurate channel estimation, leading to improved overall performance of wireless communication systems.
  • It reduces the need for manual configuration and optimization of neural networks, making the process more efficient.


Original Abstract Submitted

A method of wireless communication by a user equipment (UE) includes receiving, from a base station, a configuration to train a neural network for multiple different signal to noise ratios (SNRs) of a channel estimate for a wireless communication channel. The method also includes determining a current SNR of the channel estimate is above a first threshold value. The method further includes training the neural network based on the channel estimate, to obtain a first trained neural network. The method still further includes perturbing the channel estimate to obtain a perturbed channel estimate, and training the neural network based on the perturbed channel estimate, to obtain a second trained neural network. The method includes reporting, to the base station, parameters of the first trained neural network along with the channel estimate, and parameters of the second trained neural network.

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